def init(data):
global __data__
__data__ = data
#
fig1 = plt.figure()
plt1 = namedtuple('plot', [
'fig', 'handles'
])(fig1, namedtuple('Handles', [
'bar', 'mov_ave'
])(*new_cases(__data__.country_large, 'US', __data__.dfs, 'new_cases', 5)))
#
fig2 = plt.figure()
plt2 = namedtuple('plot', ['fig', 'handles'])(
fig2, namedtuple('Handles', ['confirmed', 'recovered', 'deaths'])(
total_numbers(__data__.country_large,
'US',
__data__.dfs,
'confirmed',
colors=['red']),
total_numbers(__data__.country_large,
'US',
__data__.dfs,
'recovered',
colors=['green']),
total_numbers(__data__.country_large,
'US',
__data__.dfs,
'deaths',
colors=['black'])))
return namedtuple('figs', ['fig1', 'fig2'])(plt1, plt2)
def _make_plot(self):
plt.close(1)
margin = {'top': 25, 'bottom': 35, 'left': 35, 'right': 25}
fig_layout = {'height': '100%', 'width': '100%'}
self.I_fig = plt.figure(1,
title='Stokes I',
fig_margin=margin,
layout=fig_layout)
self.I_plot = plt.plot(self.u, self.I)
plt.xlabel("Δλ / ΔλD")
plt.close(2)
self.Q_fig = plt.figure(2,
title='Stokes Q',
fig_margin=margin,
layout=fig_layout)
self.Q_plot = plt.plot(self.u, self.Q)
plt.xlabel("Δλ / ΔλD")
plt.close(3)
self.U_fig = plt.figure(3,
title='Stokes U',
fig_margin=margin,
layout=fig_layout)
self.U_plot = plt.plot(self.u, self.U)
plt.xlabel("Δλ / ΔλD")
plt.close(4)
self.V_fig = plt.figure(4,
title='Stokes V',
fig_margin=margin,
layout=fig_layout)
self.V_plot = plt.plot(self.u, self.V)
plt.xlabel("Δλ / ΔλD")
def create_widget(self, output, plot, dataset, limits):
self.plot = plot
self.output = output
self.dataset = dataset
self.limits = np.array(limits).tolist()
self.scale_x = bqplot.LinearScale(min=limits[0][0], max=limits[0][1])
self.scale_y = bqplot.LinearScale(min=limits[1][0], max=limits[1][1])
self.scale_rotation = bqplot.LinearScale(min=0, max=1)
self.scale_size = bqplot.LinearScale(min=0, max=1)
self.scale_opacity = bqplot.LinearScale(min=0, max=1)
self.scales = {'x': self.scale_x, 'y': self.scale_y, 'rotation': self.scale_rotation,
'size': self.scale_size, 'opacity': self.scale_opacity}
margin = {'bottom': 30, 'left': 60, 'right': 0, 'top': 0}
self.figure = plt.figure(self.figure_key, fig=self.figure, scales=self.scales, fig_margin=margin)
self.figure.layout.min_width = '900px'
plt.figure(fig=self.figure)
self.figure.padding_y = 0
x = np.arange(0, 10)
y = x ** 2
self._fix_scatter = s = plt.scatter(x, y, visible=False, rotation=x, scales=self.scales)
self._fix_scatter.visible = False
# self.scale_rotation = self.scales['rotation']
src = "" # vaex.image.rgba_to_url(self._create_rgb_grid())
# self.scale_x.min, self.scale_x.max = self.limits[0]
# self.scale_y.min, self.scale_y.max = self.limits[1]
self.image = bqplot_image.Image(scales=self.scales, src=src, x=self.scale_x.min, y=self.scale_y.max,
width=self.scale_x.max - self.scale_x.min, height=-(self.scale_y.max - self.scale_y.min))
self.figure.marks = self.figure.marks + [self.image]
# self.figure.animation_duration = 500
self.figure.layout.width = '100%'
self.figure.layout.max_width = '500px'
self.scatter = s = plt.scatter(x, y, visible=False, rotation=x, scales=self.scales, size=x, marker="arrow")
self.panzoom = bqplot.PanZoom(scales={'x': [self.scale_x], 'y': [self.scale_y]})
self.figure.interaction = self.panzoom
# self.figure.axes[0].label = self.x
# self.figure.axes[1].label = self.y
self.scale_x.observe(self._update_limits, "min")
self.scale_x.observe(self._update_limits, "max")
self.scale_y.observe(self._update_limits, "min")
self.scale_y.observe(self._update_limits, "max")
self.observe(self._update_scales, "limits")
self.image.observe(self._on_view_count_change, 'view_count')
self.control_widget = widgets.VBox()
self.widget = widgets.VBox(children=[self.control_widget, self.figure])
self.create_tools()
def create_widget(self, output, plot, dataset, limits):
self.plot = plot
self.output = output
self.dataset = dataset
self.limits = np.array(limits).tolist()
self.scale_x = bqplot.LinearScale(min=limits[0][0].item(), max=limits[0][1].item())
self.scale_y = bqplot.LinearScale(min=limits[1][0].item(), max=limits[1][1].item())
self.scale_rotation = bqplot.LinearScale(min=0, max=1)
self.scale_size = bqplot.LinearScale(min=0, max=1)
self.scale_opacity = bqplot.LinearScale(min=0, max=1)
self.scales = {'x': self.scale_x, 'y': self.scale_y, 'rotation': self.scale_rotation,
'size': self.scale_size, 'opacity': self.scale_opacity}
margin = {'bottom': 30, 'left': 60, 'right': 0, 'top': 0}
self.figure = plt.figure(self.figure_key, fig=self.figure, scales=self.scales, fig_margin=margin)
self.figure.layout.min_width = '900px'
plt.figure(fig=self.figure)
self.figure.padding_y = 0
x = np.arange(0, 10)
y = x ** 2
self._fix_scatter = s = plt.scatter(x, y, visible=False, rotation=x, scales=self.scales)
self._fix_scatter.visible = False
# self.scale_rotation = self.scales['rotation']
src = "" # vaex.image.rgba_to_url(self._create_rgb_grid())
# self.scale_x.min, self.scale_x.max = self.limits[0]
# self.scale_y.min, self.scale_y.max = self.limits[1]
self.image = bqplot_image.Image(scales=self.scales, src=src, x=self.scale_x.min, y=self.scale_y.max,
width=self.scale_x.max - self.scale_x.min, height=-(self.scale_y.max - self.scale_y.min))
self.figure.marks = self.figure.marks + [self.image]
# self.figure.animation_duration = 500
self.figure.layout.width = '100%'
self.figure.layout.max_width = '500px'
self.scatter = s = plt.scatter(x, y, visible=False, rotation=x, scales=self.scales, size=x, marker="arrow")
self.panzoom = bqplot.PanZoom(scales={'x': [self.scale_x], 'y': [self.scale_y]})
self.figure.interaction = self.panzoom
# self.figure.axes[0].label = self.x
# self.figure.axes[1].label = self.y
self.scale_x.observe(self._update_limits, "min")
self.scale_x.observe(self._update_limits, "max")
self.scale_y.observe(self._update_limits, "min")
self.scale_y.observe(self._update_limits, "max")
self.observe(self._update_scales, "limits")
self.image.observe(self._on_view_count_change, 'view_count')
self.control_widget = widgets.VBox()
self.widget = widgets.VBox(children=[self.control_widget, self.figure])
self.create_tools()
def show_cohort(y_true, protected, backend="bqplot"):
"""Show the base rates in the cohort by protected attribute."""
# jerry-rig confusion matrix to count prevalence in the population
mn, fn, mp, fp = confusion_matrix(y_true, protected).ravel()
# Now we can do two histograms:
values = [[mp, fp], [mn, fn]]
colors = ["green", "red"]
if backend == "bqplot":
fig = plt.figure(min_aspect_ratio=1, max_aspect_ratio=1)
# First index is colour, second index is X
# Note - putting negative does cool weird stuff
bars = plt.bar(
x_ticks,
values,
colors=colors,
# display_legend=False,
# labels=["Good Customers", "Bad Customers"],
)
siz = "4in"
fig.layout.width = siz
fig.layout.height = siz
else:
fig, ax = mplt.subplots(figsize=(5, 5), dpi=DPI)
ax.bar(x_ticks, values[0], color=colors[0])
ax.bar(x_ticks, values[1], bottom=values[0], color=colors[1])
return fig
def bqbars(values, colors, title, stake):
fig = plt.figure()
eo_bars = plt.bar(x_ticks, values, colors=colors)
plt.ylabel("Fraction (%)")
plt.xlabel(stake)
fig.title = title
return fig
def build_widgets(self, *args, **kwargs):
# residuals plot
self.residuals_fig = plt.figure(title='Residuals vs Predicted Values',
layout=Layout(width='960px',
height='600px',
overflow_x='hidden',
overflow_y='hidden'))
axes_options = {
'y': {
'label': 'Residual',
'tick_format': '0.1f'
},
'x': {
'label': 'Predicted Value'
}
}
self.residuals_plot = plt.scatter([], [],
colors=['yellow'],
default_size=16,
stroke='black',
axes_options=axes_options)
# zero line
plt.hline(level=0, colors=['limegreen'], stroke_width=3)
self.widgets_layout = HBox([self.residuals_fig])
def interact(self,
max_semiangle: float = None,
phi: float = 0.,
sliders=None,
throttling=False):
import bqplot.pyplot as plt
from abtem.visualize.bqplot import show_measurement_1d
from abtem.visualize.widgets import quick_sliders, throttle
import ipywidgets as widgets
figure = plt.figure(fig_margin={
'top': 0,
'bottom': 50,
'left': 50,
'right': 0
})
figure.layout.height = '250px'
figure.layout.width = '300px'
_, callback = show_measurement_1d(
lambda: self.profiles(max_semiangle, phi).values(), figure)
if throttling:
callback = throttle(throttling)(callback)
self.changed.register(callback)
if sliders:
sliders = quick_sliders(self, **sliders)
return widgets.HBox([figure, widgets.VBox(sliders)])
else:
return figure
def __init__(self, simu, net):
"""
The initializer of the Reconstruction Workbench.
Args:
simu: The simulation object to simulate
net: The network to use to reconstruct data
"""
# We check input
assert issubclass(type(simu), simulation.BaseSimulation)
assert issubclass(type(net), network.BaseNetwork)
# We instantiate arguments
self._simulation = simu
self._network = net
self._sliders = [
ipw.FloatSlider(
min=0.,
max=1.,
step=0.01,
description=self._simulation.get_factor_labels()[i])
for i in range(self._simulation.nb_params())
]
self._factors = list(np.ones(self._simulation.nb_params()))
# We generate view elements
self._simu_fig = plt.figure()
self._simu_plot = plt.heatmap(
self._simulation.draw(self._factors, depth=1))
self._recons_fig = plt.figure()
self._recons_plot = plt.heatmap(
self._simulation.draw(self._factors, depth=1))
# We create a slider with callback for each parameter
for index, slider in enumerate(self._sliders):
slider.observe(self._callback_closure(index), 'value')
self._title = ipw.HTML('<h2>Reconstruction WorkBench</h2>')
self._caption = ipw.HTML('Manipulate the generative factors:')
# We layout the different parts
left_elmts = [self._title, self._caption] + self._sliders
left_layout = ipw.Layout(padding='50px 0px 0px 0px')
left_pane = ipw.VBox(left_elmts, layout=left_layout)
self._simu_fig.layout = ipw.Layout(width='25%')
self._recons_fig.layout = ipw.Layout(width='25%')
self._layout = ipw.HBox([left_pane, self._simu_fig, self._recons_fig])
IPython.display.display(self._layout)
def LoadEvaluationPlot(result, prediction_size, conjunto, modelo):
"""Prepara debajo de la celda una gráfica que detalla visualmente el resultado
de la evaluación de un conjunto de datos utilizando un modelo específico.
Args:
result (:obj: `TestResult`): instancia de TestResult con los resultados
de la evaluación.
prediction_size (int): número de años predichos
conjunto (str): conjunto de datos evaluado.
modelo (str): método de predicción utilizado.
"""
n = int(result.Y.shape[0] / prediction_size)
m = max(np.amax(result.Y_hat), np.amax(result.Y))
colors = []
if prediction_size >= 1:
colors.append(1)
if prediction_size >= 2:
colors.append(2)
if prediction_size >= 3:
colors.append(3)
if prediction_size >= 4:
colors.append(4)
if prediction_size >= 5:
colors.append(5)
colors = np.array(colors * n)
fig = plt.figure(
title='Proyección de matrícula escolar en %s utilizando %s' %
(conjunto, modelo),
legend_location='top-left',
fig_margin=dict(top=50, bottom=70, left=100, right=100))
plt.scales(
scales={
'color':
OrdinalColorScale(
colors=['Green', 'DodgerBlue', 'Yellow', 'Orange', 'Red'])
})
axes_options = {
'x': dict(label='Valor real (alumnos)'),
'y': dict(label='Valor predicho (alumnos)'),
'color': dict(label='Año', side='right')
}
scatter2 = plt.scatter(result.Y,
result.Y_hat,
color=colors,
stroke='black',
axes_options=axes_options)
plt.plot(x=np.array([0, m]),
y=np.array([0, m]),
labels=['Línea base de predicción'],
display_legend=True)
def test_figure(self):
size = 100
scale = 100.0
np.random.seed(0)
x_data = np.arange(size)
y_data = np.cumsum(np.random.randn(size) * scale)
fig = plt.figure(title='First Example')
plt.plot(y_data)
fig.save_png()
def create_charts(self):
self.epoch_slider = IntSlider(description='Epoch:',
min=1,
max=self.num_epochs,
value=1)
self.mode_dd = Dropdown(
description='View',
options=['Weights', 'Gradients', 'Activations'],
value='Weights')
self.update_btn = Button(description='Update')
self.bar_figure = plt.figure()
self.bar_plot = plt.bar([], [], scales={'x': OrdinalScale()})
self.hist_figure = plt.figure(title='Histogram of Activations')
self.hist_plot = plt.hist([], bins=20)
self.controls = HBox(
[self.epoch_slider, self.mode_dd, self.update_btn])
self.graph.tooltip = self.bar_figure
def _make_plot(self):
plt.close(1)
fig_margin = {'top': 25, 'bottom': 35, 'left': 35, 'right':25}
fig_layout = {'height': '100%', 'width': '100%' }
layout_args = {'fig_margin': fig_margin, 'layout': fig_layout,
'max_aspect_ratio': 1.618}
self.voigt_fig = plt.figure(1, title='Voigt profile', **layout_args)
self.voigt_plot = plt.plot(self.freq, self.h, scales={'y': LogScale()})
plt.xlabel("Δν / ΔνD")
plt.close(2)
self.abs_fig = plt.figure(2, title='(αᶜ + αˡ) / α₅₀₀', **layout_args)
self.abs_plot = plt.plot(self.freq, self.xq, scales={'y': LogScale()})
plt.xlabel("Δν / ΔνD")
plt.close(3)
self.int_fig = plt.figure(3, title='Intensity', **layout_args)
self.int_plot = plt.plot(self.freq, self.prof, scales={'y': LogScale()})
plt.xlabel("Δν / ΔνD")
plt.close(4)
self.source_fig = plt.figure(4, title='Source Function', **layout_args)
self.source_plot = plt.plot(np.log10(self.tau500), self.source_function,
scales={'y': LogScale()})
plt.xlabel("lg(τ₅₀₀)")
self.tau_labels = plt.label(['τᶜ = 1', 'τˡ = 1'], colors=['black'],
x=np.array([np.log10(self.tau500_cont),
np.log10(self.tau500_line)]),
y=np.array([self.source_function_cont,
self.source_function_line]),
y_offset=-25, align='middle')
self.tau_line_plot = plt.plot(np.array([np.log10(self.tau500_line),
np.log10(self.tau500_line)]),
np.array([self.source_function_line / 1.5,
self.source_function_line * 1.5]),
colors=['black'])
self.tau_cont_plot = plt.plot(np.array([np.log10(self.tau500_cont),
np.log10(self.tau500_cont)]),
np.array([self.source_function_cont / 1.5,
self.source_function_cont * 1.5]),
colors=['black'])
def show_measurement_1d(measurements_or_func,
figure=None,
throttling=False,
**kwargs):
if figure is None:
figure = plt.figure(fig_margin={
'top': 0,
'bottom': 50,
'left': 50,
'right': 0
})
figure.layout.height = '250px'
figure.layout.width = '300px'
try:
measurements = measurements_or_func()
return_callback = True
except TypeError:
measurements = measurements_or_func
return_callback = False
lines = []
for measurement, color in zip(measurements, TABLEAU_COLORS.values()):
calibration = measurement.calibrations[0]
array = measurement.array
x = np.linspace(calibration.offset,
calibration.offset + len(array) * calibration.sampling,
len(array))
line = plt.plot(x, array, colors=[color], **kwargs)
figure.axes[0].label = format_label(measurement.calibrations[0])
lines.append(line)
# figure.axes[1].label = format_label(measurement)
if return_callback:
@throttle(throttling)
def callback(*args, **kwargs):
for line, measurement in zip(lines, measurements_or_func()):
x = np.linspace(
calibration.offset,
calibration.offset + len(array) * calibration.sampling,
len(array))
line.x = x
line.y = measurement.array
return figure, callback
else:
return figure
def generate_pie_chart(df, title="", show_decimal=False):
fig = plt.figure(title=title)
pie_chart = plt.pie(
sizes=df.values.tolist(),
labels=df.index.values.tolist(),
display_labels="outside",
colors=chart_colors[:df.index.values.size],
display_values=True,
)
if not show_decimal:
pie_chart.values_format = "0"
return fig
def addTimeseries(inMap,path,bands,new_plot):
px_series = xr.open_dataset(path)
date_start = px_series.t.min().values
date_end = px_series.t.max().values
color = ['blue','red','green','yellow']
for i,b in enumerate(bands):
x_data = px_series.t.values
y_data = px_series.to_array().loc[dict(variable=b)][:,0,0].values
x_data = x_data[~np.isnan(y_data)]
y_data = y_data[~np.isnan(y_data)]
x_data = x_data[y_data!=0]
y_data = y_data[y_data!=0]
axes_options = {'x': {'label':'Time', 'side':'bottom', 'num_ticks':8, 'tick_format':'%b %y'}, 'y': {'orientation':'vertical', 'side':'left', 'num_ticks':10}}
if i==0:
title = ''
for x in bands:
title += (x + ' ')
title += ' timeseries'
if new_plot:
inMap.figure = bqplt.figure(title=title,layout={'max_height': '250px', 'width': '600px'})
else:
if inMap.figure is not None:
pass
else:
inMap.figure = bqplt.figure(title=title,layout={'max_height': '250px', 'width': '600px'})
scatt = bqplt.scatter(x_data, y_data, labels=[b], display_legend=True, colors=[color[i]], default_size=30, axes_options=axes_options)
widget_control = WidgetControl(widget=inMap.figure, position='bottomright')
if inMap.figure_widget is not None:
inMap.map.remove_control(inMap.figure_widget)
inMap.figure_widget = widget_control
inMap.map.add_control(inMap.figure_widget)
return
def generate_group_bar(df, title="", scientific_notation=False):
fig = plt.figure(title=title)
bar_chart = plt.bar(
x=df.columns.values.tolist(),
y=df,
labels=df.index.values.tolist(),
display_legend=False,
type="grouped",
colors=chart_colors[:df.index.values.size],
)
if df.columns.name:
plt.xlabel(df.columns.name.rsplit(" ", 1)[0])
plt.ylim(0, np.amax(df.values))
if not scientific_notation:
fig.axes[1].tick_format = ".1f"
return fig
def __init__(self, simu, net, emb_index=0):
"""
The initializer of the Latent Embedding Workbench.
Args:
simu: The simulation object to simulate
net: The NN trained to extract embedding
"""
# We check input
assert issubclass(type(simu), simulation.BaseSimulation)
assert issubclass(type(net), network.BaseNetwork)
# We instantiate arguments
self._simulation = simu
self._emb_index = emb_index
self._network = net
self._sliders = [
ipw.FloatSlider(
min=0.,
max=1.,
step=0.01,
description=self._simulation.get_factor_labels()[i])
for i in range(self._simulation.nb_params())
]
self._factors = list(np.ones(self._simulation.nb_params()))
self._latent_serie = np.zeros(
[self._network.get_latent_size()[-1], NB_SAMPLES])
# We generate view elements
self._latent_fig = plt.figure()
self._latent_plot = plt.plot(range(NB_SAMPLES), self._latent_serie)
# We create a slider with callback for each parameter
for index, slider in enumerate(self._sliders):
slider.observe(self._callback_closure(index), 'value')
self._title = ipw.HTML('<h2>Latent Space WorkBench</h2>')
self._caption = ipw.HTML('Manipulate the generative factors:')
# We layout the different parts
left_elmts = [self._title, self._caption] + self._sliders
left_layout = ipw.Layout(padding='50px 0px 0px 0px')
left_pane = ipw.VBox(left_elmts, layout=left_layout)
self._latent_fig.layout = ipw.Layout(width='50%')
self._layout = ipw.HBox([left_pane, self._latent_fig])
IPython.display.display(self._layout)
def create_figure(self):
cc = self.coords
tree = self.tree
fig = plt.figure(figsize=(8,0.5))
lines = OrderedDict()
for n in tree.nodes:
c0 = cc[n]
for nn in tree.children(n):
c1 = cc[nn]
l = plt.plot([c0[0],c1[0]],[c0[1],c1[1]],'b')
lines[(n,nn)] = l
xv = [c[0] for c in (cc).values()]
yv = [c[1] for c in (cc).values()]
points = plt.plot(xv,yv,'o')
self.fig = fig
self.lines = lines
self.points = points
def generate_bar(df, title="", scientific_notation=False, small_xlabel=False):
fig = plt.figure(title=title)
x_vals = df.index.values.tolist()
if len(x_vals) > 5:
small_xlabel = True
x_titles = []
for val in x_vals:
if len(val.split(" ")) < 3:
x_titles.append(val)
else:
x_titles.append(" ".join(val.split(" ")[:2]))
bar_chart = plt.bar(x=x_titles,
y=df,
colors=chart_colors[:df.index.values.size])
if small_xlabel:
fig.axes[0].tick_style = {"font-size": "6"}
if not scientific_notation:
fig.axes[1].tick_format = ".1f"
return fig
def __init__(self,
animation_duration=100,
aspect_ratio=1,
tail_len=1000,
lim=2):
axes_options = {'x': {'label': 'x'}, 'y': {'label': 'y'}}
self.fig = plt.figure(animation_duration=animation_duration,
min_aspect_ratio=aspect_ratio,
max_aspect_ratio=aspect_ratio)
self.line = plt.plot([], [],
marker_str='b-',
axes_options=axes_options)
self.hline = plt.hline(0,
opacities=[0],
colors=['red'],
stroke_width=3)
self.scat = plt.plot([], [], 'ro')
self.tail_len = tail_len
plt.xlim(-lim, lim)
plt.ylim(-lim, lim)
def __init__(self, x_range = (0,0), y_range = None,
width=450,
height=300,
x_label=None, y_label=None,
x_axis_type=None, y_axis_type=None):
self.fig = plt.figure()
self.
self.fig, self.ax = plt.subplots(figsize = (int(width/75), int(height/75)))
self.ax.set_xlim(*x_range)
if y_range is not None:
self.ax.set_ylim(*y_range)
if x_label is not None:
self.ax.set_xlabel(x_label)
if y_label is not None:
self.ax.set_ylabel(y_label)
self.ax.grid(False)
if x_axis_type is not None:
self.ax.set_xscale(x_axis_type)
if y_axis_type is not None:
self.ax.set_yscale(y_axis_type)
def feature_byProperty(features, xProperties, seriesProperty, **kwargs):
"""Generates a Chart from a set of features. Plots property values of one or more features.
Reference: https://developers.google.com/earth-engine/guides/charts_feature#uichartfeaturebyproperty
Args:
features (ee.FeatureCollection): The features to include in the chart.
xProperties (list | dict): One of (1) a list of properties to be plotted on the x-axis; or (2) a (property, label) dictionary specifying labels for properties to be used as values on the x-axis.
seriesProperty (str): The name of the property used to label each feature in the legend.
Raises:
Exception: If the provided xProperties is not a list or dict.
Exception: If the chart fails to create.
"""
try:
df = ee_to_df(features)
if isinstance(xProperties, list):
x_data = xProperties
y_data = df[xProperties].values
elif isinstance(xProperties, dict):
x_data = list(xProperties.values())
y_data = df[list(xProperties.keys())].values
else:
raise Exception("xProperties must be a list or dictionary.")
labels = list(df[seriesProperty])
if "ylim" in kwargs:
min_value = kwargs["ylim"][0]
max_value = kwargs["ylim"][1]
else:
min_value = y_data.min()
max_value = y_data.max()
max_value = max_value + 0.2 * (max_value - min_value)
if "title" not in kwargs:
title = ""
else:
title = kwargs["title"]
if "legend_location" not in kwargs:
legend_location = "top-left"
else:
legend_location = kwargs["legend_location"]
if "display_legend" not in kwargs:
display_legend = True
else:
display_legend = kwargs["display_legend"]
fig = plt.figure(
title=title,
legend_location=legend_location,
)
if "width" in kwargs:
fig.layout.width = kwargs["width"]
if "height" in kwargs:
fig.layout.height = kwargs["height"]
bar_chart = plt.bar(x=x_data,
y=y_data,
labels=labels,
display_legend=display_legend)
bar_chart.type = "grouped"
if "colors" in kwargs:
bar_chart.colors = kwargs["colors"]
if "xlabel" in kwargs:
plt.xlabel(kwargs["xlabel"])
if "ylabel" in kwargs:
plt.ylabel(kwargs["ylabel"])
plt.ylim(min_value, max_value)
if "xlabel" in kwargs and ("ylabel" in kwargs):
bar_chart.tooltip = Tooltip(
fields=["x", "y"], labels=[kwargs["xlabel"], kwargs["ylabel"]])
else:
bar_chart.tooltip = Tooltip(fields=["x", "y"])
plt.show()
except Exception as e:
raise Exception(e)
def feature_histogram(features,
property,
maxBuckets=None,
minBucketWidth=None,
**kwargs):
"""
Generates a Chart from a set of features.
Computes and plots a histogram of the given property.
- X-axis = Histogram buckets (of property value).
- Y-axis = Frequency
Reference:
https://developers.google.com/earth-engine/guides/charts_feature#uichartfeaturehistogram
Args:
features (ee.FeatureCollection): The features to include in the chart.
property (str): The name of the property to generate the histogram for.
maxBuckets (int, optional): The maximum number of buckets (bins) to use when building a histogram;
will be rounded up to a power of 2.
minBucketWidth (float, optional): The minimum histogram bucket width, or null to allow any power of 2.
Raises:
Exception: If the provided xProperties is not a list or dict.
Exception: If the chart fails to create.
"""
import math
if not isinstance(features, ee.FeatureCollection):
raise Exception("features must be an ee.FeatureCollection")
first = features.first()
props = first.propertyNames().getInfo()
if property not in props:
raise Exception(
f"property {property} not found. Available properties: {', '.join(props)}"
)
def nextPowerOf2(n):
return pow(2, math.ceil(math.log2(n)))
def grow_bin(bin_size, ref):
while bin_size < ref:
bin_size *= 2
return bin_size
try:
raw_data = pd.to_numeric(
pd.Series(features.aggregate_array(property).getInfo()))
y_data = raw_data.tolist()
if "ylim" in kwargs:
min_value = kwargs["ylim"][0]
max_value = kwargs["ylim"][1]
else:
min_value = raw_data.min()
max_value = raw_data.max()
data_range = max_value - min_value
if not maxBuckets:
initial_bin_size = nextPowerOf2(data_range / pow(2, 8))
if minBucketWidth:
if minBucketWidth < initial_bin_size:
bin_size = grow_bin(minBucketWidth, initial_bin_size)
else:
bin_size = minBucketWidth
else:
bin_size = initial_bin_size
else:
initial_bin_size = math.ceil(data_range / nextPowerOf2(maxBuckets))
if minBucketWidth:
if minBucketWidth < initial_bin_size:
bin_size = grow_bin(minBucketWidth, initial_bin_size)
else:
bin_size = minBucketWidth
else:
bin_size = initial_bin_size
start_bins = (math.floor(min_value / bin_size) *
bin_size) - (bin_size / 2)
end_bins = (math.ceil(max_value / bin_size) * bin_size) + (bin_size /
2)
if start_bins < min_value:
y_data.append(start_bins)
else:
y_data[y_data.index(min_value)] = start_bins
if end_bins > max_value:
y_data.append(end_bins)
else:
y_data[y_data.index(max_value)] = end_bins
num_bins = math.floor((end_bins - start_bins) / bin_size)
if "title" not in kwargs:
title = ""
else:
title = kwargs["title"]
fig = plt.figure(title=title)
if "width" in kwargs:
fig.layout.width = kwargs["width"]
if "height" in kwargs:
fig.layout.height = kwargs["height"]
if "xlabel" not in kwargs:
xlabel = ""
else:
xlabel = kwargs["xlabel"]
if "ylabel" not in kwargs:
ylabel = ""
else:
ylabel = kwargs["ylabel"]
histogram = plt.hist(
sample=y_data,
bins=num_bins,
axes_options={
"count": {
"label": ylabel
},
"sample": {
"label": xlabel
}
},
)
if "colors" in kwargs:
histogram.colors = kwargs["colors"]
if "stroke" in kwargs:
histogram.stroke = kwargs["stroke"]
else:
histogram.stroke = "#ffffff00"
if "stroke_width" in kwargs:
histogram.stroke_width = kwargs["stroke_width"]
else:
histogram.stroke_width = 0
if ("xlabel" in kwargs) and ("ylabel" in kwargs):
histogram.tooltip = Tooltip(
fields=["midpoint", "count"],
labels=[kwargs["xlabel"], kwargs["ylabel"]],
)
else:
histogram.tooltip = Tooltip(fields=["midpoint", "count"])
plt.show()
except Exception as e:
raise Exception(e)
def feature_groups(features, xProperty, yProperty, seriesProperty, **kwargs):
"""Generates a Chart from a set of features.
Plots the value of one property for each feature.
Reference:
https://developers.google.com/earth-engine/guides/charts_feature#uichartfeaturegroups
Args:
features (ee.FeatureCollection): The feature collection to make a chart from.
xProperty (str): Features labeled by xProperty.
yProperty (str): Features labeled by yProperty.
seriesProperty (str): The property used to label each feature in the legend.
Raises:
Exception: Errors when creating the chart.
"""
try:
df = ee_to_df(features)
df[yProperty] = pd.to_numeric(df[yProperty])
unique_series_values = df[seriesProperty].unique().tolist()
new_column_names = []
for value in unique_series_values:
sample_filter = (df[seriesProperty] == value).map({
True: 1,
False: 0
})
column_name = str(yProperty) + "_" + str(value)
df[column_name] = df[yProperty] * sample_filter
new_column_names.append(column_name)
if "labels" in kwargs:
labels = kwargs["labels"]
else:
labels = [str(x) for x in unique_series_values]
if "ylim" in kwargs:
min_value = kwargs["ylim"][0]
max_value = kwargs["ylim"][1]
else:
min_value = df[yProperty].to_numpy().min()
max_value = df[yProperty].to_numpy().max()
max_value = max_value + 0.2 * (max_value - min_value)
if "title" not in kwargs:
title = ""
else:
title = kwargs["title"]
if "legend_location" not in kwargs:
legend_location = "top-left"
else:
legend_location = kwargs["legend_location"]
x_data = list(df[xProperty])
y_data = [df[x] for x in new_column_names]
plt.bar(x_data, y_data)
fig = plt.figure(
title=title,
legend_location=legend_location,
)
if "width" in kwargs:
fig.layout.width = kwargs["width"]
if "height" in kwargs:
fig.layout.height = kwargs["height"]
if "display_legend" not in kwargs:
display_legend = True
else:
display_legend = kwargs["display_legend"]
bar_chart = plt.bar(x_data,
y_data,
labels=labels,
display_legend=display_legend)
if "colors" in kwargs:
bar_chart.colors = kwargs["colors"]
if "xlabel" in kwargs:
plt.xlabel(kwargs["xlabel"])
if "ylabel" in kwargs:
plt.ylabel(kwargs["ylabel"])
plt.ylim(min_value, max_value)
if "xlabel" in kwargs and ("ylabel" in kwargs):
bar_chart.tooltip = Tooltip(
fields=["x", "y"], labels=[kwargs["xlabel"], kwargs["ylabel"]])
else:
bar_chart.tooltip = Tooltip(fields=["x", "y"])
plt.show()
except Exception as e:
raise Exception(e)
def feature_byFeature(features, xProperty, yProperties, **kwargs):
"""Generates a Chart from a set of features. Plots the value of one or more properties for each feature.
Reference: https://developers.google.com/earth-engine/guides/charts_feature#uichartfeaturebyfeature
Args:
features (ee.FeatureCollection): The feature collection to generate a chart from.
xProperty (str): Features labeled by xProperty.
yProperties (list): Values of yProperties.
Raises:
Exception: Errors when creating the chart.
"""
try:
df = ee_to_df(features)
if "ylim" in kwargs:
min_value = kwargs["ylim"][0]
max_value = kwargs["ylim"][1]
else:
min_value = df[yProperties].to_numpy().min()
max_value = df[yProperties].to_numpy().max()
max_value = max_value + 0.2 * (max_value - min_value)
if "title" not in kwargs:
title = ""
else:
title = kwargs["title"]
if "legend_location" not in kwargs:
legend_location = "top-left"
else:
legend_location = kwargs["legend_location"]
x_data = list(df[xProperty])
y_data = df[yProperties].values.T.tolist()
plt.bar(x_data, y_data)
fig = plt.figure(
title=title,
legend_location=legend_location,
)
if "width" in kwargs:
fig.layout.width = kwargs["width"]
if "height" in kwargs:
fig.layout.height = kwargs["height"]
if "labels" in kwargs:
labels = kwargs["labels"]
else:
labels = yProperties
if "display_legend" not in kwargs:
display_legend = True
else:
display_legend = kwargs["display_legend"]
bar_chart = plt.bar(x_data,
y_data,
labels=labels,
display_legend=display_legend)
bar_chart.type = "grouped"
if "colors" in kwargs:
bar_chart.colors = kwargs["colors"]
if "xlabel" in kwargs:
plt.xlabel(kwargs["xlabel"])
if "ylabel" in kwargs:
plt.ylabel(kwargs["ylabel"])
plt.ylim(min_value, max_value)
if "xlabel" in kwargs and ("ylabel" in kwargs):
bar_chart.tooltip = Tooltip(
fields=["x", "y"], labels=[kwargs["xlabel"], kwargs["ylabel"]])
else:
bar_chart.tooltip = Tooltip(fields=["x", "y"])
plt.show()
except Exception as e:
raise Exception(e)
from __future__ import print_function
from bqplot import pyplot as plt
from bqplot import topo_load
from bqplot.interacts import panzoom
from numpy import *
import pandas as pd
random.seed(0)
size = 100
y_data = cumsum(random.randn(size) * 100.0)
y_data_2 = cumsum(random.randn(size))
y_data_3 = cumsum(random.randn(size) * 100.)
plt.figure(1)
n = 100
x = linspace(0.0, 10.0, n)
plt.plot(x, y_data, axes_options={'y': {'grid_lines': 'dashed'}})
plt.show()
def vue_do_aper_phot(self, *args, **kwargs):
if self._selected_data is None or self._selected_subset is None:
self.result_available = False
self.results = []
self.plot_available = False
self.radial_plot = ''
self.hub.broadcast(SnackbarMessage(
"No data for aperture photometry", color='error', sender=self))
return
data = self._selected_data
reg = self._selected_subset
try:
comp = data.get_component(data.main_components[0])
try:
bg = float(self.background_value)
except ValueError: # Clearer error message
raise ValueError('Missing or invalid background value')
comp_no_bg = comp.data - bg
# TODO: Use photutils when it supports astropy regions.
if not isinstance(reg, RectanglePixelRegion):
aper_mask = reg.to_mask(mode='exact')
else:
# TODO: https://github.com/astropy/regions/issues/404 (moot if we use photutils?)
aper_mask = reg.to_mask(mode='subpixels', subpixels=32)
npix = np.sum(aper_mask) * u.pix
img = aper_mask.get_values(comp_no_bg, mask=None)
aper_mask_stat = reg.to_mask(mode='center')
comp_no_bg_cutout = aper_mask_stat.cutout(comp_no_bg)
img_stat = aper_mask_stat.get_values(comp_no_bg, mask=None)
include_pixarea_fac = False
include_counts_fac = False
include_flux_scale = False
if comp.units:
img_unit = u.Unit(comp.units)
img = img * img_unit
img_stat = img_stat * img_unit
bg = bg * img_unit
comp_no_bg_cutout = comp_no_bg_cutout * img_unit
if u.sr in img_unit.bases: # TODO: Better way to detect surface brightness unit?
try:
pixarea = float(self.pixel_area)
except ValueError: # Clearer error message
raise ValueError('Missing or invalid pixel area')
if not np.allclose(pixarea, 0):
include_pixarea_fac = True
if img_unit != u.count:
try:
ctfac = float(self.counts_factor)
except ValueError: # Clearer error message
raise ValueError('Missing or invalid counts conversion factor')
if not np.allclose(ctfac, 0):
include_counts_fac = True
try:
flux_scale = float(self.flux_scaling)
except ValueError: # Clearer error message
raise ValueError('Missing or invalid flux scaling')
if not np.allclose(flux_scale, 0):
include_flux_scale = True
rawsum = np.nansum(img)
d = {'id': 1,
'xcenter': reg.center.x * u.pix,
'ycenter': reg.center.y * u.pix}
if data.coords is not None:
d['sky_center'] = data.coords.pixel_to_world(reg.center.x, reg.center.y)
else:
d['sky_center'] = None
d.update({'background': bg,
'npix': npix})
if include_pixarea_fac:
pixarea = pixarea * (u.arcsec * u.arcsec / u.pix)
pixarea_fac = npix * pixarea.to(u.sr / u.pix)
d.update({'aperture_sum': rawsum * pixarea_fac,
'pixarea_tot': pixarea_fac})
else:
d.update({'aperture_sum': rawsum,
'pixarea_tot': None})
if include_counts_fac:
ctfac = ctfac * (rawsum.unit / u.count)
sum_ct = rawsum / ctfac
d.update({'aperture_sum_counts': sum_ct,
'aperture_sum_counts_err': np.sqrt(sum_ct.value) * sum_ct.unit,
'counts_fac': ctfac})
else:
d.update({'aperture_sum_counts': None,
'aperture_sum_counts_err': None,
'counts_fac': None})
if include_flux_scale:
flux_scale = flux_scale * rawsum.unit
d.update({'aperture_sum_mag': -2.5 * np.log10(rawsum / flux_scale) * u.mag,
'flux_scaling': flux_scale})
else:
d.update({'aperture_sum_mag': None,
'flux_scaling': None})
# Extra stats beyond photutils.
d.update({'mean': np.nanmean(img_stat),
'stddev': np.nanstd(img_stat),
'median': np.nanmedian(img_stat),
'min': np.nanmin(img_stat),
'max': np.nanmax(img_stat),
'data_label': data.label,
'subset_label': reg.meta.get('label', ''),
'timestamp': Time(datetime.utcnow())})
# Attach to app for Python extraction.
if (not hasattr(self.app, '_aper_phot_results') or
not isinstance(self.app._aper_phot_results, QTable)):
self.app._aper_phot_results = _qtable_from_dict(d)
else:
try:
d['id'] = self.app._aper_phot_results['id'].max() + 1
self.app._aper_phot_results.add_row(d.values())
except Exception: # Discard incompatible QTable
d['id'] = 1
self.app._aper_phot_results = _qtable_from_dict(d)
# Radial profile
reg_bb = reg.bounding_box
reg_ogrid = np.ogrid[reg_bb.iymin:reg_bb.iymax, reg_bb.ixmin:reg_bb.ixmax]
radial_dx = reg_ogrid[1] - reg.center.x
radial_dy = reg_ogrid[0] - reg.center.y
radial_r = np.hypot(radial_dx, radial_dy).ravel() # pix
radial_img = comp_no_bg_cutout.ravel()
if comp.units:
y_data = radial_img.value
y_label = radial_img.unit.to_string()
else:
y_data = radial_img
y_label = 'Value'
bqplt.clear()
# NOTE: default margin in bqplot is 60 in all directions
fig = bqplt.figure(1, title='Radial profile from Subset center',
fig_margin={'top': 60, 'bottom': 60, 'left': 40, 'right': 10},
title_style={'font-size': '12px'}) # TODO: Jenn wants title at bottom. # noqa
bqplt.plot(radial_r, y_data, 'go', figure=fig, default_size=1)
bqplt.xlabel(label='pix', mark=fig.marks[-1], figure=fig)
bqplt.ylabel(label=y_label, mark=fig.marks[-1], figure=fig)
except Exception as e: # pragma: no cover
self.result_available = False
self.results = []
self.plot_available = False
self.radial_plot = ''
self.hub.broadcast(SnackbarMessage(
f"Aperture photometry failed: {repr(e)}", color='error', sender=self))
else:
# Parse results for GUI.
tmp = []
for key, x in d.items():
if key in ('id', 'data_label', 'subset_label', 'background', 'pixarea_tot',
'counts_fac', 'aperture_sum_counts_err', 'flux_scaling', 'timestamp'):
continue
if (isinstance(x, (int, float, u.Quantity)) and
key not in ('xcenter', 'ycenter', 'sky_center', 'npix',
'aperture_sum_counts')):
x = f'{x:.4e}'
tmp.append({'function': key, 'result': x})
elif key == 'sky_center' and x is not None:
tmp.append({'function': 'RA center', 'result': f'{x.ra.deg:.4f} deg'})
tmp.append({'function': 'Dec center', 'result': f'{x.dec.deg:.4f} deg'})
elif key in ('xcenter', 'ycenter', 'npix'):
x = f'{x:.1f}'
tmp.append({'function': key, 'result': x})
elif key == 'aperture_sum_counts' and x is not None:
x = f'{x:.4e} ({d["aperture_sum_counts_err"]:.4e})'
tmp.append({'function': key, 'result': x})
elif not isinstance(x, str):
x = str(x)
tmp.append({'function': key, 'result': x})
self.results = tmp
self.result_available = True
self.radial_plot = fig
self.bqplot_figs_resize = [fig]
self.plot_available = True
import numpy as np import bqplot.pyplot as plt size = 100 plt.figure(title="Scatter plot with colors") plt.scatter(np.random.randn(size), np.random.randn(size), color=np.random.randn(size)) plt.show()
#!/usr/bin/env python
# coding: utf-8
from bqplot import pyplot as plt
import ipyvuetify as v
import ipywidgets as widgets
import numpy as np
# generate some fake data
np.random.seed(0)
n = 2000
x = np.linspace(0.0, 10.0, n)
y = np.cumsum(np.random.randn(n)*10).astype(int)
# create a bqplot figure
fig_hist = plt.figure(title='Histogram')
hist = plt.hist(y, bins=25)
# slider
slider = v.Slider(thumb_label='always', class_="px-4", v_model=30)
widgets.link((slider, 'v_model'), (hist, 'bins'))
fig_lines = plt.figure( title='Line Chart')
lines = plt.plot(x, y)
# even handling
selector = plt.brush_int_selector()
def update_range(*ignore):
if selector.selected is not None and len(selector.selected) == 2:
xmin, xmax = selector.selected
mask = (x > xmin) & (x < xmax)
def plot_profitability_distributions(scenarios, backend):
for id in scenarios:
# Make scenario data
scenario = scenarios[id]
desc = scenario.description
train_data = scenario.gendata("train")
test_data = scenario.gendata("test")
np.random.seed(42)
X_train, y_train = scenario.get_modelling_data(train_data)
X_test, y_test = scenario.get_modelling_data(test_data)
data = {
'training': (X_train, y_train, train_data.disadv_flag),
'deployment': (X_test, y_test, test_data.disadv_flag),
}
figure_name = f"Profitability_{id}_{desc}"
print(f"for Scenario {id}: {desc}")
if backend == "matplotlib":
l = len(data.items())
fig, ax = mplt.subplots(1, l, figsize=(5 * l, 5), dpi=DPI)
i = 0
else:
plts = []
for cohort, (X, y_true, prot) in data.items():
dis = prot.astype(int)
mn, fn, mp, fp = confusion_matrix(y_true, dis).ravel()
values = [[mp, fp], [mn, fn]]
# half way between selected and not
colors = ["#ff4045", "#48B748"]
title = f"Representation in {cohort} cohort."
ylabel = "Number of Applicants"
if backend == "matplotlib":
bars = mplt_bars(ax[i], scenario.ticks, values, colors, ylabel,
title)
mplt.legend(
bars,
["profitable customers", "non-profitable customers"],
bbox_to_anchor=(1.05, 0.5) # sit outside plot...
)
i += 1
else:
fig = plt.figure(min_aspect_ratio=1, max_aspect_ratio=1)
# First index is colour, second index is X
# Note - putting negative does cool weird stuff
bars = plt.bar(
scenario.ticks,
values,
colors=colors,
# display_legend=False,
# labels=["Good Customers", "Bad Customers"],
)
siz = "4in"
fig.layout.width = siz
fig.layout.height = siz
fig.title = title
fig.axes[0].color = fig.axes[1].color = "Black"
plt.ylabel(ylabel)
plts.append(fig)
if backend == "matplotlib":
fig.savefig("images/" + figure_name + ".png",
bbox_inches="tight",
dpi=300)
elif backend == "bqplot":
box = widgets.HBox(plts)
box.layout.width = "90%"
display(box)
figure_name = f"Profitability_{id}_{desc}"
def plot_feature_importances(scenarios, backend):
# Plot all the feature importances
for id in scenarios:
# Make scenario data
scenario = scenarios[id]
desc = scenario.description
figure_name = f"Importance_{id}_{desc}"
train_data = scenario.gendata("train")
test_data = scenario.gendata("test")
np.random.seed(42)
X_train, y_train = scenario.get_modelling_data(train_data)
X_test, y_test = scenario.get_modelling_data(test_data)
dis = scenario.ticks[1]
if dis != "SE Asian proxy": # proper noun
dis = dis.lower()
if "pinterest" in train_data.columns:
ren = {
"pinterest": scenario.proxy_name,
'disadv_flag': dis,
}
# warning: train data seems to be writable in-place
X_train = X_train.rename(columns=ren)
X_test = X_test.rename(columns=ren)
clf = LogisticRegression()
clf.fit(X_train, y_train)
columns = list(X_train.columns)
importance = clf.coef_[0]
if True:
# standardize?
importance = importance / X_train.std(axis=0)
heights = np.abs(importance)
cols = ['orange', 'blue']
colors = (np.array(cols)[(importance >= 0).astype(int)]).tolist()
title = "Model Feature Importance"
print("Importance: {}".format(importance))
if backend == "matplotlib":
fig, ax = mplt.subplots(figsize=(5, 5), dpi=DPI)
ax.bar(columns, importance, color=colors)
ax.axhline(0, color="Black", lw=1.)
# Add some dummies for a legend
c = columns[0]
mplt.bar([c], [0], color=cols[1], label="Increases Score")
mplt.bar([c], [0], color=cols[0], label="Decreases Score")
mplt.plot([-0.5, len(columns) - 0.5], [0, 0], 'k')
scale = 10
if int(id) == 5:
# this scenario uses huge weights for some reason...
scale = 40
mplt.ylim(-scale, scale)
mplt.legend(bbox_to_anchor=(1.5, 0.5))
ax.set_title(title)
fig.savefig("images/" + figure_name + ".png",
bbox_inches="tight",
dpi=300)
elif backend == "bqplot":
fig = plt.figure(title=title,
min_aspect_ratio=1,
max_aspect_ratio=1)
for c, h, colr in zip(columns, importance, colors):
plt.bar([c], [h], colors=[colr]) # each bar is its own bar
plt.ylim(-10, 10) # was -10, 10 except for scenario 5?
fig.axes[0].color = fig.axes[1].color = "Black"
fig.layout.width = fig.layout.height = "5in"
display(fig)
def create_widget(self, output, plot, dataset, limits):
self.plot = plot
self.output = output
self.dataset = dataset
self.limits = np.array(limits).tolist()
def fix(v):
# bqplot is picky about float and numpy scalars
if hasattr(v, 'item'):
return v.item()
else:
return v
self.scale_x = bqplot.LinearScale(min=fix(limits[0][0]),
max=fix(limits[0][1]),
allow_padding=False)
self.scale_y = bqplot.LinearScale(min=fix(limits[1][0]),
max=fix(limits[1][1]),
allow_padding=False)
self.scale_rotation = bqplot.LinearScale(min=0, max=1)
self.scale_size = bqplot.LinearScale(min=0, max=1)
self.scale_opacity = bqplot.LinearScale(min=0, max=1)
self.scales = {
'x': self.scale_x,
'y': self.scale_y,
'rotation': self.scale_rotation,
'size': self.scale_size,
'opacity': self.scale_opacity
}
margin = {'bottom': 35, 'left': 60, 'right': 5, 'top': 5}
self.figure = plt.figure(self.figure_key,
fig=self.figure,
scales=self.scales,
fig_margin=margin)
self.figure.layout.min_width = '600px'
plt.figure(fig=self.figure)
self.figure.padding_y = 0
x = np.arange(0, 10)
y = x**2
self._fix_scatter = s = plt.scatter(x,
y,
visible=False,
rotation=x,
scales=self.scales)
self._fix_scatter.visible = False
# self.scale_rotation = self.scales['rotation']
src = "" # vaex.image.rgba_to_url(self._create_rgb_grid())
# self.scale_x.min, self.scale_x.max = self.limits[0]
# self.scale_y.min, self.scale_y.max = self.limits[1]
self.core_image = widgets.Image(format='png')
self.core_image_fix = widgets.Image(format='png')
self.image = bqplot.Image(scales=self.scales, image=self.core_image)
self.figure.marks = self.figure.marks + [self.image]
# self.figure.animation_duration = 500
self.figure.layout.width = '100%'
self.figure.layout.max_width = '500px'
self.scatter = s = plt.scatter(x,
y,
visible=False,
rotation=x,
scales=self.scales,
size=x,
marker="arrow")
self.panzoom = bqplot.PanZoom(scales={
'x': [self.scale_x],
'y': [self.scale_y]
})
self.figure.interaction = self.panzoom
for axes in self.figure.axes:
axes.grid_lines = 'none'
axes.color = axes.grid_color = axes.label_color = blackish
self.figure.axes[0].label = str(plot.x)
self.figure.axes[1].label = str(plot.y)
self.scale_x.observe(self._update_limits, "min")
self.scale_x.observe(self._update_limits, "max")
self.scale_y.observe(self._update_limits, "min")
self.scale_y.observe(self._update_limits, "max")
self.observe(self._update_scales, "limits")
self.image.observe(self._on_view_count_change, 'view_count')
self.control_widget = widgets.VBox()
self.widget = widgets.VBox(children=[self.figure])
self.create_tools()
